XKeyword provides efficient keyword proximity queries on large XML graph databases. A query is simply a list of keywords and does not require any schema or query language knowledge for its formulation. XKeyword is built on a relational database and, hence, can accommodate very large graphs. Query evaluation is optimized by using the graph's schema. In particular, XKeyword consists of two stages. In the preprocessing stage a set of keyword indices are built along with indexed path relations that describe particular patterns of paths in the graph. In the query processing stage plans are developed that use a near optimal set of path relations to efficiently locate the keyword query results. The results are presented graphically using the novel idea of interactive result graphs, which are populated on-demand according to the user's navigation and allow efficient information discovery. We provide theoretical and experimental points for the selection of the appropriate set of precomputed path relations. We also propose and experimentally evaluate algorithms to minimize the number of queries sent to the database to output the top-K results.